Explicit signals personalized search

ABSTRACT

Methods, systems, and computer program products are provided that enable users to provide explicit declarations that are used to generate recommendations for the users. An explicit declaration is received from a user of a user device. The explicit declaration is configured to influence a subsequent recommendation. The words of the explicit declaration are processed to generate a record. A recommendation rule is generated based on the generated record. The recommendation rule is executed to generate a recommendation for the user. The generated recommendation is provided to the user.

BACKGROUND

Within the field of computing, many systems exist that can inferinformation about an individual based on various signals and use suchinformation to provide the individual with enhanced services andpersonalized content. For example, an e-commerce Web site may recommendproducts of interest to an individual based on inferences drawn from theindividual's previous purchases. As another example, a search engine maypersonalize search results and related content to a user based oninferences made regarding the user. Traditionally, search engines derivesuch inferences from user queries, clicks, views, and occasionally bydata mining social networks.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

Methods, systems, and computer program products are provided that enableusers to provide explicit declarations that are used to personalizerecommendations for the users. Such explicit declarations provideinformation regarding who the users are, and what the users actuallywant to see. A user may speak or otherwise provide an explicitdeclaration in a manner that indicates the explicit declaration is to beused to influence subsequent recommendations. A recommendation may begenerated and provided to the user immediately after the user providesthe explicit declaration, or the recommendation may be provided later.The recommendation may be triggered to be provided to the user based ona time, a location of the user, one or more persons with the user, anactivity of the user, a request from the user for data, a request fromthe user for a recommendation, and/or another trigger.

In one exemplary method implementation, an explicit declaration isreceived from a user of a device. The explicit declaration is configuredby the user to influence a subsequent recommendation. The words of theexplicit declaration are processed to generate a record. Arecommendation rule is generated based on the generated record. Therecommendation rule is executed to generate a recommendation for theuser. The generated recommendation is provided to the user.

For instance, in one aspect, natural language processing may beperformed on a series of words of the explicit declaration to extractone or more key features. The record may be generated to include theextracted key feature(s). The extracted key feature(s) may be used togenerate the recommendation rule corresponding to the explicitdeclaration.

In an aspect, it may be determined whether there is a match between therule and an environmental trigger. Examples of environmental triggersinclude a current time, a future time, a current location of the user, afuture location of the user, a person with the user, an activity of theuser, an application or service, a request from the user for data, arequest from the user for a recommendation, etc. When there is a match,the matching recommendation rule is executed to generate therecommendation for the user.

In another implementation, a recommendation system that is implementedin one or more computing devices is provided. The recommendation systemincludes a voiced input interface, a speech processing module, a rulesgenerator, and a recommendation engine. The voiced input interface isconfigured to receive an explicit declaration from a user of a computingdevice. The explicit declaration includes a series of words, and isconfigured to influence a subsequent recommendation. The speechprocessing module is configured to process the series of words of theexplicit declaration to generate a record. The rules generator isconfigured to generate a recommendation rule based on the generatedrecord. The recommendation engine is configured to execute therecommendation rule to generate a recommendation for the user.

A computer readable storage medium is also disclosed herein havingcomputer program instructions stored therein that enables a user toprovide an explicit declaration, and causes a recommendation to begenerated for the user based at least in part on the explicitdeclaration, according to the embodiments described herein.

Further features and advantages of the invention, as well as thestructure and operation of various embodiments of the invention, aredescribed in detail below with reference to the accompanying drawings.It is noted that the invention is not limited to the specificembodiments described herein. Such embodiments are presented herein forillustrative purposes only. Additional embodiments will be apparent topersons skilled in the relevant art(s) based on the teachings containedherein.

BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES

The accompanying drawings, which are incorporated herein and form a partof the specification, illustrate embodiments of the present applicationand, together with the description, further serve to explain theprinciples of the embodiments and to enable a person skilled in thepertinent art to make and use the embodiments.

FIG. 1 shows a block diagram of a system in which a user device thatincludes an explicit signal-enabled recommendation system communicateswith one or more servers and/or other user devices to exchangeinformation, according to an example embodiment.

FIG. 2 shows a flowchart providing a process to generate arecommendation for a user based on an explicit declaration, according toan example embodiment.

FIG. 3 shows a flowchart providing an example implementation of theflowchart of FIG. 2, according to an embodiment.

FIG. 4 shows a block diagram of user device that optionally communicateswith a server to generate a recommendation for a user based on anexplicit declaration, according to an example embodiment.

FIG. 5 shows a flowchart providing a process for processing a receivedexplicit declaration to generate a record, according to an exampleembodiment.

FIG. 6 shows a block diagram of a system used to process a receivedexplicit declaration to generator records and rules, according to anexample embodiment.

FIG. 7 shows a flowchart providing a process for executingrecommendation rules to generate a recommendation for a user, accordingto an example embodiment.

FIG. 8 shows a block diagram of a recommendation engine that receivesrecommendation rules to generate recommendations for a user, accordingto an example embodiment.

FIG. 9 shows a block diagram of an exemplary user device in whichembodiments may be implemented.

FIG. 10 shows a block diagram of an example computing device that may beused to implement embodiments.

The features and advantages of the present invention will become moreapparent from the detailed description set forth below when taken inconjunction with the drawings, in which like reference charactersidentify corresponding elements throughout. In the drawings, likereference numbers generally indicate identical, functionally similar,and/or structurally similar elements. The drawing in which an elementfirst appears is indicated by the leftmost digit(s) in the correspondingreference number.

DETAILED DESCRIPTION I. Introduction

The present specification and accompanying drawings disclose one or moreembodiments that incorporate the features of the present invention. Thescope of the present invention is not limited to the disclosedembodiments. The disclosed embodiments merely exemplify the presentinvention, and modified versions of the disclosed embodiments are alsoencompassed by the present invention. Embodiments of the presentinvention are defined by the claims appended hereto.

References in the specification to “one embodiment,” “an embodiment,”“an example embodiment,” etc., indicate that the embodiment describedmay include a particular feature, structure, or characteristic, butevery embodiment may not necessarily include the particular feature,structure, or characteristic. Moreover, such phrases are not necessarilyreferring to the same embodiment. Further, when a particular feature,structure, or characteristic is described in connection with anembodiment, it is submitted that it is within the knowledge of oneskilled in the art to effect such feature, structure, or characteristicin connection with other embodiments whether or not explicitlydescribed.

Numerous exemplary embodiments are described as follows. It is notedthat any section/subsection headings provided herein are not intended tobe limiting. Embodiments are described throughout this document, and anytype of embodiment may be included under any section/subsection.Furthermore, embodiments disclosed in any section/subsection may becombined with any other embodiments described in the samesection/subsection and/or a different section/subsection in any manner.

Embodiments described herein enable personalized recommendations to begenerated for users based on explicit declarations. The user may speakor otherwise provide the explicit declaration (e.g., by text entry,etc.) to a computing device (also referred to as “user device”). Forinstance, the user device may include a digital personal assistant orother entity configured to handle explicit declarations as disclosedherein. An explicit declaration is configured to influence a subsequentrecommendation to be made to the user that provided the explicitdeclaration. Such an explicit declaration is made by the user in “realtime,” while the user is using their device in a routine manner. Suchreal time providing of an explicit declaration is distinguished from atraining routine, where one or more questions and/or choices may beposed to the user to answer. In contrast, an explicit declaration isprovided by a user without prompting by the user device.

An explicit declaration may provide a rating or a review of a thing(e.g., “Remember that I really didn't like the food at the Baja BeachHouse”), may identify a thing of interest (or not of interest) to theuser, and/or may provide other information that the user may want to beused to influence and thereby personalize subsequent recommendations.

In an embodiment, when making an explicit declaration, a user mayaddress the explicit declaration to the user device, such as bybeginning the explicit declaration with a digital personal assistantidentifier/name (e.g., “Assistant, remember that . . . ”). In thismanner, the user device may recognize that an explicit declaration isbeing provided. However, this is not required in all embodiments. Inanother embodiment, the user device may be configured to receiveexplicit declarations from the user even if the explicit declaration isnot addressed to a digital personal assistant. For instance, the usermay make a statement to a friend that is overheard by the user device,and the user device may recognize and process the statement as anexplicit declaration. In such an embodiment, the user may be required to“opt-in” so that the user device is enabled to listen in and processexplicit declarations in this manner.

For instance, the user may provide an explicit declaration to a searchengine with words that indicate that a recommendation system shouldremember it for subsequent application, such as by stating “Rememberthat Sophie says I should try Thai Tom restaurant” (i.e., telling therecommendation system to remember a recommendation received from anotherperson regarding a thing), “Remember that I would like to check out aLondon museum sometime” (e.g., telling the recommendation system toremind the user about a thing based on the user's own observation orthought), etc.

Accordingly, one or more explicit declarations may be received from auser, and may be converted into rules or other form that may beprocessed to generate recommendations for the user. For instance, one ormore environmental triggers may occur, such as a time, a location of theuser, the presence of one or more persons with the user, and/or anactivity of the user. The occurrence of a particular environmentaltrigger may cause a corresponding recommendation to be generated andprovided to the user.

Recommendations may be provided to a user based on explicit declarationsin any suitable environment. For instance, FIG. 1 shows a block diagramof a communication system 100, according to an example embodiment. Insystem 100, a user may interact with a first user device 102 to provideexplicit declarations and receive recommendations based thereon. Asshown in FIG. 1, system 100 includes first user device 102, a back endserver 104, a first server 106 a, a second server 106 b, and a seconduser device 108. Furthermore, first user device 102 includes a userinterface 122 and an on-device personal electronic assistant 112, whichincludes an explicit signal-enabled recommendation system 114. Stillfurther, back end server 104 includes a network-based assistant service120, first server 106 a includes a third party service 116, and secondserver 106 b includes a third party service 118. The features of system100 are described as follows.

First user device 102 and second user device 108 may each be any type ofstationary or mobile computing device, including a mobile computer ormobile computing device (e.g., a Microsoft® Surface® device, a personaldigital assistant (PDA), a laptop computer, a notebook computer, atablet computer such as an Apple iPad™, a netbook, etc.), a mobile phone(e.g., a cell phone, a smart phone such as a Microsoft Windows® phone,an Apple iPhone, a phone implementing the Google® Android™ operatingsystem, a Palm® device, a Blackberry® device, etc.), a wearablecomputing device (e.g., a smart watch, a head-mounted device includingsmart glasses such as Google® Glass™ etc.), or other type of mobiledevice (e.g., an automobile), or a stationary computing device such as adesktop computer or PC (personal computer). Still further, first userdevice 102 and second user device 108 may each be a portable mediaplayer, a stationary or handheld gaming console, a personal navigationassistant, a camera, or other type of stationary or mobile device.Although first and second user devices 102 and 108 are shown in FIG. 1,in other embodiments, other numbers of user devices may be present insystem 100, including tens, hundreds, thousands, and millions of userdevices.

Back end server 104, first server 106 a, and second server 106 b mayeach be formed of one or more computing devices capable of servinginformation. Fewer numbers or greater numbers of servers than shown inFIG. 1 may be present in system 100 in embodiments.

Each of user devices 102 and 108, and servers 104, 106 a, and 106 b mayinclude a network interface that enables communications over network110. Such a network interface may include one or more of any type ofnetwork interface (e.g., network interface card (NIC)), wired orwireless, such as an as IEEE 802.11 wireless LAN (WLAN) wirelessinterface, a Worldwide Interoperability for Microwave Access (Wi-MAX)interface, an Ethernet interface, a Universal Serial Bus (USB)interface, a cellular network interface, a Bluetooth™ interface, a nearfield communication (NFC) interface, etc. Further examples of networkinterfaces are described elsewhere herein. Examples of network 110include a local area network (LAN), a wide area network (WAN), apersonal area network (PAN), and/or a combination of communicationnetworks, such as the Internet.

User interface 122 enables a user to submit questions, commands, orother verbal and/or non-verbal input and delivers responses to suchinput to the user. In one embodiment, the input may comprise user speechthat is captured by one or more microphones of user device 102, althoughthis example is not intended to be limiting and user input may beprovided in other ways as well (e.g., textually, etc.). The responsesgenerated by electronic personal assistant 112 may be made visible tothe user in the form of text, images, or other visual content shown byuser interface 122 on a display of user device 102 (e.g., within agraphical user interface). The responses may also comprisecomputer-generated speech or other audio content that is played back viaone or more speakers of user device 102. Still further, the responses(visual content and/or audio content) may also be provided to the uservia a display, one or more speakers, and/or other user interface outputdevice that is/are physically separate from user device 102, butcommunicatively coupled with user device 102 (e.g., by a WiFi or WLANinterface, a USB interface, a cellular network interface, a Bluetooth™interface, a near field communication (NFC) interface, a MirrorLink™interface, a Miracast™ interface, etc.).

Electronic personal assistant 112 is an application (e.g., one or morecomputer programs that execute in processor(s) of user device 102) thata user can interact with through user interface 122 of user device 102.Electronic personal assistant 112 is configured to provide generalassistance to a user by handling requests provided by the user, such asrequests to perform tasks and/or services. For instance, in embodiments,electronic personal assistant 112 may be configured to answer questions,make recommendations, and/or perform actions.

Electronic personal assistant 112 may be fully contained in user device102, or may further include a network/cloud-based back end to handlerequests and/or to delegate requests to a set of web services. Anexample of such a back end service is shown included in back end server104 as network-based assistant service 120. Network-based assistantservice 120 may be a back-end portion of an electronic personalassistant service that includes both on-device electronic personalassistant 112 and network-based assistant service 120. Network-basedassistant service 120 may enable further processing/reasoning to beperformed with regard to providing assistance to users. Furthermore,network-based assistant service 120 may interact with services at one ormore services, such as third party service 116 at first server 106 a andthird party service 118 at second server 106 b, to retrieve information,to delegate information processing, etc., that may be used to assistelectronic personal assistant 112 to provide assistance to users.Alternatively, electronic personal assistant 112 may access servicessuch as third party services 116 and 118 directly (as indicated bydotted arrows in FIG. 1).

As such, electronic personal assistant 112 (with or withoutnetwork-based assistant service 120) may be an electronic personalassistant, a search engine, or other system configured to providegeneral user assistance. Examples of commercially available searchengines that are applicable to electronic personal assistant 112 includeMicrosoft® Bing™ (at http://www.bing.com) and Google™ (athttp://www.google.com). Examples of commercially available electronicpersonal assistants that are applicable to electronic personal assistant112 include Siri® developed by Apple Inc. of Cupertino, Calif., andGoogle Now™ developed of Mountain View, Calif.

In embodiments, electronic personal assistant 112 may handle the userrequests based on user input as well as based on features such aslocation awareness and the ability to access information from a varietyof sources including online sources (such as weather or trafficconditions, news, stock prices, user schedules, retail prices, etc.).Examples of tasks that may be performed by electronic personal assistant112 on behalf of a user may include, but are not limited to, placing aphone call to a user-specified person, launching a user-specifiedapplication, sending a user-specified e-mail or text message to auser-specified recipient, playing user-specified music, scheduling ameeting or other event on a user calendar, obtaining directions to auser-specified location, obtaining a score associated with auser-specified sporting event, posting user-specified content to asocial media web site or microblogging service, recording user-specifiedreminders or notes, obtaining a weather report, obtaining the currenttime, setting an alarm at a user-specified time, obtaining a stock pricefor a user-specified company, finding a nearby commercial establishment,performing an Internet search, or the like. Electronic personalassistant 112 may use any of a variety of artificial intelligencetechniques to improve its performance over time through continuedinteraction with the user. In some embodiments, electronic personalassistant 112 may also be referred to as a search engine, a digitalpersonal assistant, an intelligent personal assistant, an intelligentsoftware assistant, a virtual personal assistant, or the like.

As shown in FIG. 1, electronic personal assistant 112 includes explicitsignal-enabled recommendation system 114. Explicit signal-enabledrecommendation system 114 enables electronic personal assistant 112 toreceive explicit declarations from a user and generate recommendationsfor the user that are based in part on the explicit declarations.

Note that in an embodiment, a portion of explicit signal-enabledrecommendation system 114 may be included in network-based assistantservice 120 at back end server 104. As such, network-based assistantservice 120 may enable further reasoning to be performed with regard toexplicit declarations, to be used to generate recommendations for theuser. For instance, network-based assistant service 120 may provideincreased processing/computing power relative to user device 102, mayprovide search engine capability, may provide an increased understandingof the user by maintaining or accessing social network information orother profile information for the user (e.g., at one or both of thirdparty services 116 and/or 118), and/or by providing other functionality.

For instance, third party service 116 at server 106 a may optionally bepresent, and may be a social network that maintains social networkinformation including a social network profile for the user. Electronicpersonal assistant 112 and/or network-based assistant service 120 mayaccess the social network profile information through network 110. Thesocial network information may be used by electronic personal assistant112 and/or network-based assistant service 120 in addition to anexplicit declaration to generate a recommendation for the user.

Third party service 118 at server 106 a may also optionally be present(as well as further third party optional services), and may generateand/or store information that may be used with explicit declaration 124by electronic personal assistant 112 and/or network-based assistantservice 120 to assist in generating a recommendation for the user. Forexample, third party service 118 may be a restaurant recommendationservice, a travel recommendation website, a product recommendationwebsite, or other site or service that maintains user profileinformation, makes recommendations, and/or stores or generates relevantinformation regarding the user.

In an embodiment, explicit signal-enabled recommendation system 114 mayoperate according to FIG. 2. FIG. 2 shows a flowchart 200 providing aprocess in a user device to generate a recommendation for a user basedon an explicit declaration, according to an example embodiment.Flowchart 200 is described as follows with respect to FIG. 1. Furtherstructural and operational embodiments will be apparent to personsskilled in the relevant art(s) based on the following description.

Flowchart 200 begins with step 202. In step 202, an explicit declarationis received from a user of the user device. For example, with referenceto FIG. 1, a user may be enabled to provide an explicit declaration 124to explicit signal-enabled recommendation system 114 by interacting withuser interface 122. The user may speak into a microphone of userinterface 122 to provide explicit declaration 124, or may provideexplicit declaration 124 textually or in another manner. Explicitdeclaration 124 may be provided as a series of words or in anothermanner, and is provided by the user with the intent to influence asubsequent recommendation. Explicit declaration 124 is received byexplicit signal-enabled recommendation system 114 through user interface122.

For instance, in one illustrative example of explicit declaration 124,the user may say “Assistant, remind me to check out the Sohoneighborhood sometime.” Through this explicit declaration, the user isindicating their intention for electronic personal assistant 112, whichis addressed as “Assistant” by the user in this example, to provide themwith a future recommendation with respect to the Soho neighborhood. Thewords “remind me,” “remember,” or similar words directed to theAssistant indicate the user's intention for the Assistant to record thisexplicit declaration for a future recommendation.

In step 204, a recommendation is generated for the user based in part onthe explicit declaration. Explicit signal-enabled recommendation system114 of electronic personal assistant 112 (optionally in conjunction withnetwork-based assistant service 120) is configured to generate arecommendation for the user based at least on explicit declaration 124.The recommendation may further be generated based on inferredinformation regarding the user, user profile information regarding theuser, and/or based on other information related to the user, as well asone or more environmental triggers. As shown in FIG. 1, user interface122 may provide a recommendation 126 generated by explicitsignal-enabled recommendation system 114 to the user. Recommendation 126may be provided in the form of speech or other audio played from one ormore speakers of user interface 122, may be displayed as text in userinterface 122, or may be provided to the user in another manner by userinterface 122.

Continuing the above illustrative example, recommendation 126 may beplayed to the user as “Hello Sophie, you are now near the Sohoneighborhood that you wanted to check out—you should try Mexican food atLa Esquina.” In another example, recommendation 126 may be provided tothe user as a displayed list, such as:

Hello Sophie!

You wanted to check out Soho, which is nearby. Here are some Sohorestaurants to try:

-   -   La Esquina    -   Dos Caminos Soho        In such an example, the user may be enabled to select a listed        recommended restaurant to be displayed a location of the        restaurant (and optionally a route to the restaurant).

In these example recommendations, the Assistant is indicating to theuser (“Sophie”, in this example) a recommendation to try a restaurant inthe Soho neighborhood, based at least on the explicit declarationprovided to the Assistant by the user. This recommendation may have beentriggered based on one or more environmental triggers, such as alocation of the user (e.g., being near the Soho neighborhood), a time(e.g., being near meal time), etc. In one example, once it wasdetermined that the user was near the Soho neighborhood, a search enginequery may be triggered for a restaurant to recommend in the Sohoneighborhood. Accordingly, in an embodiment, the generation of arecommendation may include the issuing of a query to generate one ormore items to be included in the recommendation. The search query thatresulted in one or more restaurants being recommended may have had anysuitable form such as being a multi-word query for Soho attractions(e.g., “Soho tourist attractions”), a query for Soho restaurants (e.g.,“Soho restaurants”), a query for a type of restaurant favored by Sophiebased on her profile (“Soho Mexican restaurants”), etc.

Note that in the embodiment of FIG. 1, recommendation 126 is provided tothe user by the same user device (user device 102) to which explicitdeclaration 124 was provided. Note that in another embodiment,recommendation 126 may be provided to the user by a different devicethan the device to which explicit declaration 124 was provided.

Accordingly, in embodiments, a user may be enabled to provide anexplicit declaration, and a subsequent recommendation may be generatedfor the user based at least in part on the explicit declaration. Furtherdetails of these and further embodiments are provided in the followingsections. For instance, Section II, which follows this section,describes exemplary methods and systems for providing explicitdeclarations and generating recommendations. Section III describesexemplary mobile and desktop computing devices that may be used toimplement embodiments described herein. Section IV provides someconcluding remarks.

II. Example Embodiments for Providing Explicit Declarations andGenerating Recommendations

As described above, explicit signal-enabled recommendation system 114may be configured in various ways to enable explicit declarations to beprovided and to generate recommendations. For instance, FIG. 3 shows aflowchart 300 providing a process to generate a recommendation based onan explicit declaration, according to an embodiment. Explicitsignal-enabled recommendation system 114 may operate according toflowchart 300, in an embodiment. Flowchart 300 is described as followswith respect to FIG. 4. FIG. 4 shows a block diagram of user device 102and server 104 of FIG. 1 configured to generate a recommendation for auser based on an explicit declaration, according to an exampleembodiment. As shown in FIG. 4, user device 102 includes on-deviceassistant service 112, which includes an explicit signal-enabledrecommendation system (RS) 432. Explicit signal-enabled RS 432 is anexample of explicit signal-enabled RS 114. Explicit signal-enabled RS432 includes a speech processing module 404, a rules generator 406, arecommendation engine 408, and a UI (user interface) 430. Server 104includes network-based assistant service 120, which includes an explicitsignal enabled RS 434. Explicit signal-enabled RS 434 includes a rulesgenerator 412 and a recommendation engine 414.

Explicit signal-enabled RS 434 at server 104 and explicit signal-enabledRS 432 of user device 102 may form a combined explicit signal-enabledRS, or may be separate from each other. Explicit signal-enabled RS 434and explicit signal-enabled RS 432 do not need to both be present in allembodiments.

Note that in one embodiment, flowchart 300 may be entirely performed inuser device 102. In another embodiment, user device 102 and server 104may each perform portions of flowchart 300. Further structural andoperational embodiments will be apparent to persons skilled in therelevant art(s) based on the following description.

Flowchart 300 of FIG. 3 begins with step 302. In step 302, an explicitdeclaration is received from a user of the user device, the explicitdeclaration configured to influence a subsequent recommendation. Withreference to FIG. 4, a user may be enabled to provide an explicitdeclaration 416 to explicit signal-enabled RS 432 by interacting with avoiced-input interface 402 provided by UI 430. UI 430 is an example ofuser interface 122 of FIG. 1, and in an embodiment, voiced-inputinterface 402 includes a microphone and circuitry configured to receiveexplicit declaration 416 in the form of speech. In another embodiment,UI 430 may include a non-voiced input interface 436, which may include atext entry interface, a push-button (e.g., physical and/or virtualbuttons), and/or other interface, to receive explicit declaration 416 inanother form. Explicit declaration 416 may be provided as a series ofwords or in other form, and is provided by the user with the intent toinfluence a subsequent recommendation. Explicit declaration 416 may bereceived from voiced-input interface 402 of UI 430 by speech processingmodule 404 as series of words 418. Series of words 418 may be providedin the form of recorded speech or other form providing spoken words.Alternatively, explicit declaration 416 may be received by speechprocessing module 404 from non-voiced interface 436 of UI 430 asnon-verbal declaration indication 438. Non-verbal declaration indication438 may be provided in the form of text, data indicated by buttonselection, or other form, depending on how explicit declaration 416 isreceived from the user by non-voiced interface 436.

In step 304, the explicit declaration is processed to generate a record.For instance, in an embodiment, speech processing module 404 isconfigured to process series of words 418 to generate a record 420.Record 420 is representative of explicit declaration 416, and mayoptionally be stored in storage (not shown in FIG. 4). For instance,record 420 may include one or more of the contents of series of words418, a time at which explicit declaration 416 was received, a locationat which explicit declaration 416 was received, and any informationgenerated by speech processing module 404 by processing series of words418 (e.g., a location identified in series of words 418, a personidentified in series of words 418, a time identified in series of words418, a context identified for series of words 418, and/or an affinityidentified in series of words 418). Series of words 418 may be processedin any manner, including using speech recognition (if provided explicitdeclaration 416 is provided in the form of speech), by the parsing ofseries of words 418 to perform key feature extraction, as well asperforming or applying natural language processing, machine learning,artificial intelligence, and/or other processes to derive a meaning ofexplicit declaration 416.

For example, in an embodiment, step 304 may be performed according toFIG. 5. FIG. 5 shows a flowchart 500 for processing a received explicitdeclaration to generate a record, according to an example embodiment.For illustrative purposes, flowchart 500 is described with reference toFIG. 6. FIG. 6 shows a block diagram of speech processing module 404 andrules generator 602 in association with a storage 604, according to anexample embodiment. Further structural and operational embodiments willbe apparent to persons skilled in the relevant art(s) based on thefollowing description.

Flowchart 500 begins with step 502. In step 502, natural languageprocessing is performed on the series of words of the explicitdeclaration to extract one or more key features. For instance, as shownin FIG. 6, speech processing module 404 may optionally include a dataconverter 620. Data converter 620 is configured to receive non-verbaldeclaration indication 438 (when present). As described above,non-verbal declaration indication 438 may indicate an explicitdeclaration provided in the form of text, as data indicated by buttonselection, or indicated in another form. Data converter 620 may beconfigured to convert non-verbal declaration indication 438 into one ormore words (e.g., similar to series of words 418), which is output bydata converter 620 as converted non-verbal declaration 622. In thismanner, an explicit declaration provided in the form of button pushes,etc., can be converted into words that can be processed by speechprocessing module 404 for meaning. For example, a button labeled as“Remember that” (or similarly labeled) that is followed by a searchresults string, a recommendation provided by another person, etc., dataconverter 620 may generate converted non-verbal declaration 622 toinclude a string that includes the words “Remember that” (or otherlabel) combined with the search results string, recommendation text,etc.

It is noted that when non-verbal declaration indication 438 indicates anexplicit declaration received from the user in the form of text (e.g.,typed into a text entry box, or otherwise received as text from theuser), data converter 620 may not be necessary (and non-verbaldeclaration indication 438 may be provided to natural languageprocessing module 606 for processing as described below).

Examples of non-verbal explicit declarations that may be provided by auser and received in non-verbal declaration indication 438 includeindications of where a user clicked on a physical or virtual button toprovide an explicit declaration (e.g., by clicking a Facebook® “like”button on an entity, rating a restaurant highly on a restaurant reviewwebsite such as Yelp!® at www.yelp.com, indicating something as afavorite, etc.). In such case, metadata associated with the click orbutton push may be provided in non-verbal declaration indication 438. Inan example case, a user may be provided with a recommendation by anotherperson, and the user may interact with UI 430 to accept therecommendation. For instance, Sophie may share a recommendation withAnna, and Anna may receive the recommendation from Sophie in a message(e.g., an email, a text message, an instant message, a social networkmessage, etc.). Anna may be enabled by UI 430 to merely say the word“accept” or press a virtual button labeled “accept” to provide herexplicit declaration that she wants her Assistant (e.g., on-deviceassistant service 112) to remember the recommendation from Sophie. Inanother embodiment, Anna's Assistant may be enabled to silently acceptSophie's recommendation when it is received from Sophie on Anna'sbehalf. For instance, Anna's Assistant may detect the recommendation inthe message received from Anna. The silent acceptance by Anna'sAssistant may be enabled based on a previous conclusion that Anna islikely to trust Sophie, that Anna has proactively indicated (e.g., in UI430) that she wants to automatically accept Sophie's recommendations,etc.

Furthermore, speech processing module 404 may include a natural languageprocessing module 606 that is configured to perform natural languageprocessing (NLP) on series of words 418 (and/or on converted non-verbaldeclaration 622). In an embodiment, the natural language processing mayextract key features from series of words 418 and/or convertednon-verbal declaration 622 by one or more of generating a parse tree,performing word sense disambiguation, performing named-entityrecognition, performing natural language understanding, etc. Keyfeatures such as location words (or “tokens”), person-related words,time-related words, words expressing affinities, and/or context-relatedwords may be extracted therefrom.

For example, in an embodiment, NLP module 606 may perform parsing onseries of words 418 and/or converted non-verbal declaration 622 toextract one or more words categorized in one or more of the key featuretypes listed in Table 1 below:

TABLE 1 Location Time Person(s) Context Affinities City or SpecificDate/ Anonymous People (e.g., Food named Time personali- geographiczation, location timing) Area (e.g., Event Sphere Crowd RecentActivities neighbor- (happy hour, Events hood) etc.) Proper PlaceArbitrary Social Calendar Stores/ (e.g., a Future (to be NetworkShopping specific checked out museum, a at some specific arbitraryrestaurant) future time) Derived Negotiated/ Friends Location TravelPlace (e.g., Scheduled (an coordinate at indicated where future the usertime) is at a time) Milestones Authorities Resources Long (e.g.,birthday, (e.g., Decisions anniversary, time, (e.g., etc.) money)research for buying a car or a house, moving to a new city) SimilarsEnvironment (persons (weather, with similar safety) tastes orpreferences as the user) Health (e.g., life stage)In Table 1, each column is directed to a particular feature typecategory—location, time, people, context, and affinities. Furthermore,each feature type category may have one or more sub-categories,non-exhaustive examples of which are shown in the corresponding columnsof Table 1.

Thus, NLP module 606 may categorize one or more words of series of words418 and/or converted non-verbal declaration 622 into these and/or otherfeature type categories. This categorization may be used by NLP module606 to perform natural language understanding of series of words 418and/or converted non-verbal declaration 622. Note that, however, thecategories of Table 1 are provided for purposes of illustration, and arenot intended to be limiting. NLP module 606 may categorize words intoany types of categories, including those categories provided in Table 1,and/or any other categories that are otherwise known and/or may beapparent to persons skilled in the relevant art(s). Any particular wordor series of words may be categorized by NLP module 606 into any numbersof categories, including a single category, or multiple categories. Inembodiments, a recommendation may be formed based on words/datacategorized into any number of categories.

Referring back to FIG. 5, in step 504, the record is generated toinclude the extracted one or more key features. For instance, as shownin FIG. 6, speech processing module 404 generates record 420corresponding to a received explicit declaration. As shown in FIG. 6,record 420 may be stored in storage 604 in a user record log 610 as anew record 614 n. Information that indicates the natural languageunderstanding of series of words 418 and/or converted non-verbaldeclaration 622 may be stored in new record 614 a with the extracted keyfeatures, including the information described above with respect torecord 420 (FIG. 4). User record log 610 is a log of records generatedbased on explicit declarations provided by the user. As shown in FIG. 6,the user may have records 614 a-614 n stored in user record log 610,corresponding to a number “n” of explicit declarations received from theuser. As described further below with respect to rules generator 602,one or more rules may be generated based on each of records 614 a-614 nto be used to generate recommendations for the user.

In one illustrative example of flowchart 500, a first person named Anna(the user) may be talking to her friend Sophie (a second person). Sophiemay mention an amazing new restaurant (“Thai Tom”) to Anna that she saysAnna has to try. Anna may pull out her smart phone (e.g., user device102), activate her electronic personal assistant, and say “Assistant,remember that Sophie says I should try Thai Tom.” The electronicpersonal assistant may respond “Got it,” or may provide anotherconfirmation of receiving the explicit declaration.

Accordingly, NLP module 606 of speech processing module 404 may performnatural language processing of this received explicit declaration toparse key features such as “remember” (an indication of an intent forthe Assistant to use this statement as an explicit declaration for afuture recommendation), “Sophie” as a second person, “I should try” asindicating Sophie provided a recommendation to the user, and “Thai Tom”as the recommended location for Sophie to try. This information may beindicated in a record, such as record 614 n, in any manner. For example,record 614 n may be stored as a data structure with attributes andvalues corresponding to the key features category names and key featurevalues, as well as storing any additional information regarding theexplicit declaration mentioned elsewhere herein.

Referring back to FIG. 4, note that although speech processing module404 is shown being located in user device 102, in another embodiment,some or all speech processing of received explicit declarations mayinstead be performed at server 104.

Referring back to flowchart 300 in FIG. 3, in step 306, a recommendationrule is generated based on the generated record. For example, as shownin FIG. 4, rules generator 406 and recommendation engine 408 may beincluded in explicit signal-enabled RS 432 (user device 102), and rulesgenerator 412 and recommendation engine 414 may be included in explicitsignal-enabled RS 434 (server 104), depending on the particularimplementation. For instance, if user device 102 has sufficientprocessing power, and access to the appropriate data resources, rulesgenerator 406 and recommendation engine 408 may process records togenerate rules and recommendations based on records of explicitdeclarations. In another embodiment, it may be desired to use rulesgenerator 412 and recommendation engine 414 at server 104 to generaterules and/or recommendation based on records of explicit declarationsbecause greater processing capacity may be available at server 104,because desired data resources may be more easily accessed at server104, and/or for other reasons. In embodiments, any combination of rulesgenerator 406, recommendation engine 408, rules generator 412, andrecommendation engine 414 may be used to generate rules and/orrecommendations as described herein.

Depending on the particular embodiment, rules generator 406 and/or rulesgenerator 412 may receive record 420. Rules generator 406 and/or rulesgenerator 412 may be configured to generate one or more recommendationrules based on record 420. Each recommendation rule defines when and howa corresponding recommendations is provided. The recommendation rulesmay have any desired format. For example, rules generator 406 and/orrules generator 412 may generate logic based rules using operators suchas OR, XOR, AND, ELSE, NOT, IF-THEN, etc, and/or may generaterecommendation rules in other formats. As shown in FIG. 4, rulesgenerator 406 (user device 102) may generate a recommendation rule 422based on record 420, and rules generator 412 (server 104) may generate arecommendation rule 426 based on record 420.

For illustrative purposes, rules generator 406 and rules generator 412are described in further detail with respect to FIG. 6. FIG. 6 shows arules generator 602 that is an example embodiment of rules generator 406and of rules generator 412. Rules generator 602 is configured togenerate one or more recommendation rules based on record 420. As shownin FIG. 6, in an embodiment, rules generator 602 may include a logicalstatement generator 608. Logical statement generator 608 is configuredto receive record 420, and generate one or more corresponding rules(e.g., logical expressions or statements) based on logical operators,such as IF-THEN rules, etc. For purposes of illustration, IF-THEN rulesare shown in examples as follows, although in embodiments any format ofrules may be used. Each IF-THEN rule has a format of IF <first logicalexpression> THEN <second logical expression>. The first logicalexpression defines the conditions for matching the rule, and the secondlogical expression defines what action is performed when the rule ismatched according to the first logical expression.

As shown in FIG. 6, logical statement generator 608 is configured tostore generated rules in storage 604. For example, logical statementgenerator 608 stores a new rule 616 m corresponding to record 412 instorage 604. Rules that are generated for the user may be stored in auser rules log 612 corresponding to the user. As shown in FIG. 6, userrules log 612 includes first-mth rules 616 a-616 m. First-mth rules 616a-616 m may include one or more rules corresponding to each of records614 a-614 n.

Referring to the above example of Sophie providing a recommendation of“Thai Tom” to Anna, logical statement generator 608 may generate thefollowing recommendation rule:

-   -   IF <user requests restaurant> THEN <recommend restaurant “Thai        Tom”>        In this example, if Anna asks her digital personal assistant to        recommend a restaurant to her, the above IF logical statement is        matched. Accordingly, the recommendation of the restaurant “Thai        Tom” will be provided to her per the THEN logical statement.

Note that IF-THEN statements of any complexity may be generated bylogical statement generator 608, such that the first logical statementmay have more than one condition to be satisfied, and the second logicalstatement may provide more than one recommendation action. For example,referring to the above example of Sophie providing a recommendation of“Thai Tom” to Anna, Anna may have a preference indicated in a userprofile model 618 for only being recommended restaurants within 10 milesof her current location. Accordingly, logical statement generator 608may generate the following recommendation rule:

-   -   IF <user requests restaurant AND restaurant “Thai Tom” is within        10 miles of current location> THEN <recommend restaurant “Thai        Tom”>        In this example, if Anna asks her digital personal assistant to        recommend a restaurant to her and Anna is located within 10        miles of restaurant “Thai Tom” (as determined by a location        determiner of user device 102, such as a GPS module, etc.), the        recommendation of the restaurant “Thai Tom” will be provided to        her. Otherwise, Anna will not be provided with the        recommendation.

Note that user profile model 618 shown stored in storage 604 contains acollection of personal data associated with the user (including herrestaurant distance preference) Each user may have a corresponding userprofile model. User profile model 618 may indicate information that isdescriptive of a user, such as the user's name and age, interests,skills and knowledge, preferences, dislikes, etc. The informationincluded in user profile model 618 may be provided proactively by theuser (e.g., by training), or may be determined implicitly, by trackingbehavior of the user in visiting websites, interacting with pages,interacting with other users, etc. A user may be enabled to opt-in andopt-out from having some or all aspects of user profile model 618 beingmaintained for the user. User profile model 618 may be static or may bedynamic. User profile model 618 may incorporate social networkinformation for the user (e.g., an indication of friends, familymembers, liked entities, etc.).

User profile model 618 may be stored and/or represented in any format.In an embodiment, user profile model 618 may be represented as a graphfor a user. Nodes of the graph may represent entities with which theuser has relationships (e.g., persons, places, objects, etc.) and eachnode may have a weight indicating the strength of the relationship withthe user (e.g., liked or disliked). Weighted lines may be connectedbetween the nodes that indicate the strength of the relationshipsbetween the different nodes. Alternatively, user profile model 618 maybe represented in other ways.

Storage 604 (and user profile model 618, when present) may be located inuser device 102, back end server 104, OR another server (e.g., server106 a, server 106 b, etc.), or may be distributed across multipledevices and/or servers. Storage 604 may include one or more of any typeof storage medium/device to store data, including a magnetic disc (e.g.,in a hard disk drive), an optical disc (e.g., in an optical disk drive),a magnetic tape (e.g., in a tape drive), a memory device such as a RAMdevice, a ROM device, etc., and/or any other suitable type of storagemedium/device.

Referring back to flowchart 300 in FIG. 3, in step 308, therecommendation rule is executed to generate a recommendation for theuser. For example, as shown in FIG. 4, recommendation engine 408 (userdevice 102) may receive recommendation rule 422 from rules generator 406and/or recommendation engine 414 (server 104) may receive recommendationrule 426 generated by rules generator 412. Recommendation engine 408and/or recommendation engine 414 may execute recommendation rule 422and/or 426 to provide a recommendation to the user.

For example, when recommendation engine 408 (user device 102) executesrecommendation rule 422, recommendation engine 408 may generate arecommendation 424. Recommendation 424 is provided to UI 430 to beprovided to the user by an output interface 430 of UI 430 as providedrecommendation 426. Output interface 430 may be a speaker that playsprovided recommendation 426 in the form of speech or other audio, may bea graphical user interface (GUI) that displays provided recommendation426 in the form or text or graphics, or may have another form used toprovide provided recommendation 426 to the user.

Alternatively, when recommendation engine 414 (server 104) executesrecommendation rule 426, recommendation engine 414 may generate arecommendation 428 that is transmitted to user device 102.Recommendation 428 may be provided to output interface 410 asrecommendation 424 by recommendation engine 408, to be provided to theuser as provided recommendation 426, or may be provided to the user inanother manner.

Recommendation engine 408 and/or recommendation engine 414 may execute arecommendation rule in any manner in step 308. For instance, FIG. 7shows a flowchart 700 providing a process for executing recommendationrules to generate a recommendation for a user, according to an exampleembodiment. For illustrative purposes, flowchart 700 is described withreference to FIG. 8. FIG. 8 shows a block diagram of a recommendationengine 802 that receives recommendation rules to generaterecommendations for a user, according to an example embodiment.Recommendation engine 802 is an example of recommendation engine 408 andof recommendation engine 414. Further structural and operationalembodiments will be apparent to persons skilled in the relevant art(s)based on the following description.

Flowchart 700 begins with step 702. In step 702, the recommendation ruleis matched with one or more environmental triggers. For example,recommendation engine 802 may receive one or more environmental triggers804. Environmental trigger(s) 804 may include one or more of a currenttime, a future time, a current location, a future location (e.g., apredicted or known/planned future location), a person currently with theuser, a person to be with the user in the future (e.g., a personpredicted or known/planned to be with the user at a future time and/orlocation), an activity of the user, a signal received from anapplication or service, an indication by the user that the user wants arecommendation, etc. The application or service may reside on userdevice 102 or be remote (e.g., at second user device 108, third partyservice 116 at server 106 a, third party service 118 at server 106 b,etc.).

For instance, as described above, times, locations, and/or persons maybe environmental triggers. In one example, the user Anna may meet upwith Sophie, and the presence of Sophie may be an environmental triggerto Anna's recommendation system. The presence of Sophie may be detectedin any manner by Anna's user device (e.g., by location determination ofSophie's own mobile device, by detecting Sophie's voice, by Anna beingdetected as saying “Hi Sophie” or other greeting, by Anna providing anindication on a UI of the user device, etc.). The presence of Sophie maytrigger one or more recommendations for Anna around Sophie.

In another example, the user may provide the statement of “Find me arestaurant for next Saturday night.” Such a statement could be used topredict a future location, such as Chicago, to be an environmentaltrigger for a recommendation, if the recommendation system knows thatthe user is planning to be in Chicago in that time frame (e.g., from theuser's calendar, a prior statement made by the user, etc.). Predictionsof future locations and/or persons to be with the user in the future maybe determined from historical travel patterns of the user, observedplans such as calendar events, learned data from other Assistantinteractions, or inherent properties of the location itself. Forinstance, if “Alinea” is a restaurant that only exists in Chicago, andif the user recommends Alinea as a restaurant, metadata associated withrestaurant Alinea may indicate additional features such as the location.For restaurants or other businesses that have many locations (e.g.,“Starbucks®”), additional information may be used to improve theprediction (e.g., other statements by the user, etc.).

According to step 702, the one or more indicated environmental triggersare compared with recommendation rules 616 a-616 m of user rules log612. If a match is found with one or more of recommendation rules 616a-616 m, one or more corresponding recommendations may be executed.

For instance, in an embodiment, environmental trigger(s) 804 may becompared against the IF logical statements of recommendation rules 616a-616 m when recommendation rules 616 a-616 m are IF-THEN recommendationrules. Any of recommendation rules 616 a-616 m having an IF logicalstatement matching an environmental trigger may be selected forexecution.

Continuing the above example of Sophie providing a recommendation of“Thai Tom” to Anna, Anna may be looking for a quick late night mealafter class. As such, Anna may ask her electronic personal assistant,“What's a good place for dinner?” This request for a dinnerrecommendation may be provided as an environmental trigger 804 torecommendation engine 802. Accordingly, recommendation engine 802 mayattempt to match the environmental trigger of a request for a restaurantrecommendation with rules 616 a-616 n in user rules log 612 for theuser. Recommendation engine 802 may find a match with the followingrecommendation rule:

-   -   IF <user requests restaurant> then <recommend restaurant “Thai        Tom”>        Note that the IF logical statement of this recommendation rule        may include further constraints, such as a location constraint,        a time constraint, etc. for a match to be made. As such, the        current time, the current location, and/or other environmental        triggers may be provided to recommendation engine in        environmental trigger(s) 804 to be matched with rules 616 a-616        n along with the restaurant recommendation request.

In step 704, the recommendation rule is executed with respect to the oneor more environmental triggers to generate the recommendation for theuser. In an embodiment, as shown in FIG. 8, if recommendation engine 802determines a match in step 702, recommendation 802 is configured toprovide the recommendation corresponding to the matching recommendationrule as recommendation 424 or 428. Note that if multiple recommendationrules are determined to match in step 702, the matching recommendationrules may be prioritized in any manner, and provided to the user in anorder determined by the prioritization. Alternatively, just the highestpriority recommendation may be provided to the user.

Note that in another embodiment, in addition to, or alternatively togenerating the recommendation for the user in step 704, a recommendationrule may be executed that is configured to collect additional data.Thus, rather than providing the user with a recommendation in step 704,a recommendation rule may be configured to collect additional data fromthe user and/or from other source (e.g., from another application orservice, etc.). For example, the recommendation rule may cause aquestion to be posed to the user (e.g., “You are now in the Sohoneighborhood that you asked me to remember and it is time to eat. Whatkind of food are you in the mood for?”). The answer to the question maybe used to generate a recommendation for the user right after the useranswers the question, after one or more further questions are posed,and/or at some other later time.

In an embodiment, when recommendation rules 616 a-616 m are IF-THENrecommendation rules, recommendation engine 802 may perform the actionof the THEN logical statement for the matching recommendation rule(s).For instance, continuing the above example of Sophie providing arecommendation of “Thai Tom” to Anna, recommendation engine 802 mayprovide Anna with the restaurant “Thai Tom” as a recommendation inresponse to her question “what's a good place for dinner?” due to thematch found with the corresponding IF logical statement (user requestsrestaurant).

Note that in one example, the THEN logical statement for arecommendation rule may include an action, such as an action to providea particular query to a query processing entity such as a search engine.In the current example of Sophie and Anna, the THEN logical statementmay be a query for nearby Thai food restaurants. In this manner,multiple search result suggestions may be provided in response to amatch.

In another example, recommendation rules may be configured to remindusers of aspects of recommendations when environmental triggers for therecommendations are known to occur in the future. For instance, aperson, Bob, may tell the user to check out the Alinea restaurant fordinner the next time the user is in Chicago. Accordingly, the user maytell the Assistant “Remember that I should check out the Alinearestaurant next time I am in Chicago.” A recommendation rule can beconfigured to remind the user during the planning of their next trip toChicago (which may be planned for the following month) that areservation with the restaurant Alinea should be booked (e.g., becausethe restaurant is known to be popular). By reminding the user to bookthe reservation during the planning stage for the trip, when the useractually arrives in Chicago on the trip, the reservation is already made(it is not too late to make the reservation).

In an embodiment, the multiple suggestions may be ranked based on userprofile information of the user included in user profile model 618 ofthe user. For instance, if Sophie is valued relatively highly by Anna,as indicated in user profile model 618, Sophie's recommendation of “ThaiTom” may be ranked highly, if not on the top if a list of suggestedrestaurants. If Sophie is not valued highly by Anna, Sophie'srecommendation of “Thai Tom” may be ranked lowly, or not included in thelist of suggested restaurants at all.

Note that in embodiments, explicit actions can modify or inform otherimplicit signals. For instance, if Anna actioned Sophie's recommendationfor the restaurant “Thai Tom” above (e.g., by going to and beingdetected at the location of the Thai Tom restaurant), future restaurantrankings provided to Anna may take into account Sophie's preferences. Insuch an embodiment, rules generator 602 and/or recommendation engine 802may take into account Sophie's preferences when generating rules and/orrecommendations. Sophie may make her preferences available to Anna, andthereby to rules generator 602, at a service (e.g., a social network orelsewhere) such as third party service 116 of FIG. 1, or at second userdevice 108 (which may be Sophie's user device). For instance, rulesgenerator 602 and/or recommendation engine 802 may be enabled to accessa user profile model of Sophie at one or more of these locations.

In such case, Sophie can become a marked high “influencer” for searchresults and recommendations provided to Anna in the future. This mayalso be reversed, such that because Anna actioned and/or liked Sophie'srecommendation, Sophie could be provided with user profile informationof Anna's to be used to modify search results and recommendationsprovided to Sophie.

In an embodiment, Anna may take one or more negative actions, such asgoing to the restaurant Sophie recommended, and then scoring therestaurant low on a review, or telling her digital personal assistantthat she did not like the restaurant. Such a negative action may providea strong negative signal to cause Sophie to be a low influencer withrespect to Anna (including being a negative influencer), and/or todecrease or prevent user profile information of Anna from being providedto Sophie. Accordingly, a status of a person providing a recommendationas an influencer on the user may be adjusted to be higher or lowerdepending on a reaction by the user to the recommendation. For example,if the user reacts positively (e.g., actioned and/or liked arecommendation), the status of the person providing the recommendationmay be moved in the direction of being a high influencer with respect tothe user. If the user reacts negatively (e.g., did not action and/orindicated a dislike for a recommendation), the status of the personproviding the recommendation may be moved in the direction of being alow influencer with respect to the user.

Note that in one embodiment, a second user (e.g., Sophie) may beconsidered as a high or low influencer across all domains (e.g.,restaurants, movies, travel locations, activities, clothes or otherretail items, etc.) for a first user (e.g., Anna). In anotherembodiment, the second user may be considered as a high or lowinfluencer for the first user on a domain-by-domain basis. For instance,Sophie may be considered a high influencer for Anna with respect torestaurant recommendation (e.g., because they have similar taste infood), while Sophie may be considered a low influencer for Anna withrespect to movie recommendations (e.g., because they do not share thesame taste in movies).

In this manner, a user profile model for a user may be built to includea profile of interests and trusted influencers based on real worldinteractions (e.g., did the user actually put the recommendation to useor otherwise interact with the suggestion?). Accordingly, based on themodifications to the user profile model of the user, future searchresults and recommendations for the user may be tuned.

In an embodiment, these techniques may be extended by building chit-chatscenarios around interest categories, particularly if the digitalpersonal assistant did not previously know these categories wereinterests of the user. For instance, a particular explicit declarationprovided by a user may be used as a launching point for an immediateget-to-know-you question and answer session by the digital personalassistant. For example, a user may provide the following explicitdeclaration: “Assistant, remind me to try and see Gravity in the theateronce it's out.” In response, the digital personal assistant may state“Cool, I'll remind you,” and may follow this with one or more questionstatements such as “I didn't know you were a sci-fi fan, I'll rememberthat. Or, do you just adore George Clooney?”

Note that in embodiments, searches and recommendations may originateacross devices, and may be tuned by explicit or implicit signalsreceived from any of the devices linked to a user.

In another embodiment, an explicit declaration does not need not beprecise to a single entity (e.g., about a particular restaurant such as“Thai Tom”), but can also be more general. For example, the followingexplicit declaration may be provided by a user: “Assistant, remind me tocheck out this neighborhood some other time.” This general statement ofa neighborhood may be used to train the recommendation engine to suggestexploring that neighborhood on a weekend, to rank businessrecommendations associated with that neighborhood higher. In anotherembodiment, instead of a single restaurant type, a cuisine (e.g.,Mexican) or chain (“McDonalds®”) could be expressed. Such generalitiesmay be used in any type of explicit declaration.

In embodiments, explicit declarations can contain multiple aspects oftuning information. For instance, the following example explicitdeclaration of “Assistant, remind me that Miles and I should shop atUrban Outfitters™ sometime” communicates both an affinity for a place(Urban Outfitters™) and an affinity for going to that place with aspecific person (Miles). Similarly, affinities could exist to otherinformation types such as important dates, such as in the exampleexplicit declaration: “remind me to check out the Bounce Palace when I'mplanning Luca's next birthday party.”

In an embodiment, explicit declarations may be correlated with locationsharing and a referral element to comprise a commerce offering. Forexample, Sophie may receive a discount at the “Thai Tom” restaurant nexttime that she goes there because she referred Anna to the restaurant(“e.g., Anna may have received a “friend of Sophie” discount when Annachecked in to the restaurant, or was tagged with a Bluetooth beacon orother proof of her presence at the restaurant).

Still further, a shared list of map favorites may be used as analternative way of entering Anna's explicit recommendations intoSophie's digital personal assistant's notes. For example, Anna may beenabled to say “oh, I'll share you my list of favorite Chicagorestaurants.” When she does this, Sophie can tell her digital personalassistant to remember the whole list or pick a few that seemparticularly noteworthy.

III. Example Mobile and Stationary Device Embodiments

User device 102, back end server 104, server 106 a, server 106 b, seconduser device 108, on-device assistant service 112, explicitsignal-enabled recommendation system 114, third party service 116, thirdparty service 118, network-based assistant service 120, voiced-inputinterface 402, speech processing module 404, rules generator 406,recommendation engine 408, output interface 410, UI module 412, rulesgenerator 412, recommendation engine 414, explicit signal-enabledrecommendation system 432, explicit signal-enabled recommendation system434, non-voiced input interface 436, NLP module 606, logical statementgenerator 608, data converter 620, recommendation engine 802, flowchart200, flowchart 300, flowchart 500, and flowchart 700 may be implementedin hardware, or hardware combined with software and/or firmware. Forexample, on-device assistant service 112, explicit signal-enabledrecommendation system 114, third party service 116, third party service118, network-based assistant service 120, voiced-input interface 402,speech processing module 404, rules generator 406, recommendation engine408, output interface 410, UI module 412, rules generator 412,recommendation engine 414, explicit signal-enabled recommendation system432, explicit signal-enabled recommendation system 434, non-voiced inputinterface 436, NLP module 606, logical statement generator 608, dataconverter 620, and/or recommendation engine 802, as well as one or moresteps of flowchart 200, flowchart 300, flowchart 500, and/or flowchart700 may be implemented as computer program code/instructions configuredto be executed in one or more processors and stored in a computerreadable storage medium. Alternatively, user device 102, back end server104, server 106 a, server 106 b, second user device 108, on-deviceassistant service 112, explicit signal-enabled recommendation system114, third party service 116, third party service 118, network-basedassistant service 120, voiced-input interface 402, speech processingmodule 404, rules generator 406, recommendation engine 408, outputinterface 410, UI module 412, rules generator 412, recommendation engine414, explicit signal-enabled recommendation system 432, explicitsignal-enabled recommendation system 434, non-voiced input interface436, NLP module 606, logical statement generator 608, data converter620, and/or recommendation engine 802, as well as one or more steps offlowchart 200, flowchart 300, flowchart 500, and/or flowchart 700 may beimplemented as hardware logic/electrical circuitry.

For instance, in an embodiment, one or more, in any combination, ofon-device assistant service 112, explicit signal-enabled recommendationsystem 114, third party service 116, third party service 118,network-based assistant service 120, voiced-input interface 402, speechprocessing module 404, rules generator 406, recommendation engine 408,output interface 410, UI module 412, rules generator 412, recommendationengine 414, explicit signal-enabled recommendation system 432, explicitsignal-enabled recommendation system 434, non-voiced input interface436, NLP module 606, logical statement generator 608, data converter620, recommendation engine 802, flowchart 200, flowchart 300, flowchart500, and/or flowchart 700 may be implemented together in a SoC. The SoCmay include an integrated circuit chip that includes one or more of aprocessor (e.g., a central processing unit (CPU), microcontroller,microprocessor, digital signal processor (DSP), etc.), memory, one ormore communication interfaces, and/or further circuits, and mayoptionally execute received program code and/or include embeddedfirmware to perform functions.

FIG. 9 shows a block diagram of an exemplary mobile device 900 includinga variety of optional hardware and software components, shown generallyas components 902. For instance, components 902 of mobile device 900 areexamples of components that may be included in user device 102, back endserver 104, server 106 a, server 106 b, and/or second user device 108,in mobile device embodiments. Any number and combination of thefeatures/elements of components 902 may be included in a mobile deviceembodiment, as well as additional and/or alternative features/elements,as would be known to persons skilled in the relevant art(s). It is notedthat any of components 902 can communicate with any other of components902, although not all connections are shown, for ease of illustration.Mobile device 900 can be any of a variety of mobile devices described ormentioned elsewhere herein or otherwise known (e.g., cell phone,smartphone, handheld computer, Personal Digital Assistant (PDA), etc.)and can allow wireless two-way communications with one or more mobiledevices over one or more communications networks 904, such as a cellularor satellite network, or with a local area or wide area network.

The illustrated mobile device 900 can include a controller or processorreferred to as processor circuit 910 for performing such tasks as signalcoding, image processing, data processing, input/output processing,power control, and/or other functions. Processor circuit 910 is anelectrical and/or optical circuit implemented in one or more physicalhardware electrical circuit device elements and/or integrated circuitdevices (semiconductor material chips or dies) as a central processingunit (CPU), a microcontroller, a microprocessor, and/or other physicalhardware processor circuit. Processor circuit 910 may execute programcode stored in a computer readable medium, such as program code of oneor more applications 914, operating system 912, any program code storedin memory 920, etc. Operating system 912 can control the allocation andusage of the components 902 and support for one or more applicationprograms 914 (a.k.a. applications, “apps”, etc.). Application programs914 can include common mobile computing applications (e.g., emailapplications, calendars, contact managers, web browsers, messagingapplications) and any other computing applications (e.g., wordprocessing applications, mapping applications, media playerapplications).

As illustrated, mobile device 900 can include memory 920. Memory 920 caninclude non-removable memory 922 and/or removable memory 924. Thenon-removable memory 922 can include RAM, ROM, flash memory, a harddisk, or other well-known memory storage technologies. The removablememory 924 can include flash memory or a Subscriber Identity Module(SIM) card, which is well known in GSM communication systems, or otherwell-known memory storage technologies, such as “smart cards.” Thememory 920 can be used for storing data and/or code for running theoperating system 912 and the applications 914. Example data can includeweb pages, text, images, sound files, video data, or other data sets tobe sent to and/or received from one or more network servers or otherdevices via one or more wired or wireless networks. Memory 920 can beused to store a subscriber identifier, such as an International MobileSubscriber Identity (IMSI), and an equipment identifier, such as anInternational Mobile Equipment Identifier (IMEI). Such identifiers canbe transmitted to a network server to identify users and equipment.

A number of programs may be stored in memory 920. These programs includeoperating system 912, one or more application programs 914, and otherprogram modules and program data. Examples of such application programsor program modules may include, for example, computer program logic(e.g., computer program code or instructions) for implementing on-deviceassistant service 112, explicit signal-enabled recommendation system114, third party service 116, third party service 118, network-basedassistant service 120, voiced-input interface 402, speech processingmodule 404, rules generator 406, recommendation engine 408, outputinterface 410, UI module 412, rules generator 412, recommendation engine414, explicit signal-enabled recommendation system 432, explicitsignal-enabled recommendation system 434, non-voiced input interface436, NLP module 606, logical statement generator 608, data converter620, recommendation engine 802, flowchart 200, flowchart 300, flowchart500, and/or flowchart 700 (including any suitable step of flowcharts200, 300, 500, and 700), and/or further embodiments described herein.

Mobile device 900 can support one or more input devices 930, such as atouch screen 932, microphone 934, camera 936, physical keyboard 938and/or trackball 940 and one or more output devices 950, such as aspeaker 952 and a display 954. Touch screens, such as touch screen 932,can detect input in different ways. For example, capacitive touchscreens detect touch input when an object (e.g., a fingertip) distortsor interrupts an electrical current running across the surface. Asanother example, touch screens can use optical sensors to detect touchinput when beams from the optical sensors are interrupted. Physicalcontact with the surface of the screen is not necessary for input to bedetected by some touch screens. For example, the touch screen 932 may beconfigured to support finger hover detection using capacitive sensing,as is well understood in the art. Other detection techniques can beused, as already described above, including camera-based detection andultrasonic-based detection. To implement a finger hover, a user's fingeris typically within a predetermined spaced distance above the touchscreen, such as between 0.1 to 0.25 inches, or between 0.25 inches and0.05 inches, or between 0.5 inches and 0.75 inches or between 0.75inches and 1 inch, or between 1 inch and 1.5 inches, etc.

The touch screen 932 is shown to include a control interface 992 forillustrative purposes. The control interface 992 is configured tocontrol content associated with a virtual element that is displayed onthe touch screen 932. In an example embodiment, the control interface992 is configured to control content that is provided by one or more ofapplications 914. For instance, when a user of the mobile device 900utilizes an application, the control interface 992 may be presented tothe user on touch screen 932 to enable the user to access controls thatcontrol such content. Presentation of the control interface 992 may bebased on (e.g., triggered by) detection of a motion within a designateddistance from the touch screen 932 or absence of such motion. Exampleembodiments for causing a control interface (e.g., control interface992) to be presented on a touch screen (e.g., touch screen 932) based ona motion or absence thereof are described in greater detail below.

Other possible output devices (not shown) can include piezoelectric orother haptic output devices. Some devices can serve more than oneinput/output function. For example, touch screen 932 and display 954 canbe combined in a single input/output device. The input devices 930 caninclude a Natural User Interface (NUI). An NUI is any interfacetechnology that enables a user to interact with a device in a “natural”manner, free from artificial constraints imposed by input devices suchas mice, keyboards, remote controls, and the like. Examples of NUImethods include those relying on speech recognition, touch and stylusrecognition, gesture recognition both on screen and adjacent to thescreen, air gestures, head and eye tracking, voice and speech, vision,touch, gestures, and machine intelligence. Other examples of a NUIinclude motion gesture detection using accelerometers/gyroscopes, facialrecognition, 3D displays, head, eye, and gaze tracking, immersiveaugmented reality and virtual reality systems, all of which provide amore natural interface, as well as technologies for sensing brainactivity using electric field sensing electrodes (EEG and relatedmethods). Thus, in one specific example, the operating system 912 orapplications 914 can comprise speech-recognition software as part of avoice control interface that allows a user to operate the device 900 viavoice commands. Further, device 900 can comprise input devices andsoftware that allows for user interaction via a user's spatial gestures,such as detecting and interpreting gestures to provide input to a gamingapplication.

Wireless modem(s) 960 can be coupled to antenna(s) (not shown) and cansupport two-way communications between processor circuit 910 andexternal devices, as is well understood in the art. The modem(s) 960 areshown generically and can include a cellular modem 966 for communicatingwith the mobile communication network 904 and/or other radio-basedmodems (e.g., Bluetooth 964 and/or Wi-Fi 962). Cellular modem 966 may beconfigured to enable phone calls (and optionally transmit data)according to any suitable communication standard or technology, such asGSM, 3G, 4G, 5G, etc. At least one of the wireless modem(s) 960 istypically configured for communication with one or more cellularnetworks, such as a GSM network for data and voice communications withina single cellular network, between cellular networks, or between themobile device and a public switched telephone network (PSTN).

Mobile device 900 can further include at least one input/output port980, a power supply 982, a satellite navigation system receiver 984,such as a Global Positioning System (GPS) receiver, an accelerometer986, and/or a physical connector 990, which can be a USB port, IEEE 1394(FireWire) port, and/or RS-232 port. The illustrated components 902 arenot required or all-inclusive, as any components can be not present andother components can be additionally present as would be recognized byone skilled in the art.

Furthermore, FIG. 10 depicts an exemplary implementation of a computingdevice 1000 in which embodiments may be implemented. For example, userdevice 102, back end server 104, server 106 a, server 106 b, and/orsecond user device 108 may be implemented in one or more computingdevices similar to computing device 1000 in stationary computerembodiments, including one or more features of computing device 1000and/or alternative features. The description of computing device 1000provided herein is provided for purposes of illustration, and is notintended to be limiting. Embodiments may be implemented in further typesof computer systems, as would be known to persons skilled in therelevant art(s).

As shown in FIG. 10, computing device 1000 includes one or moreprocessors, referred to as processor circuit 1002, a system memory 1004,and a bus 1006 that couples various system components including systemmemory 1004 to processor circuit 1002. Processor circuit 1002 is anelectrical and/or optical circuit implemented in one or more physicalhardware electrical circuit device elements and/or integrated circuitdevices (semiconductor material chips or dies) as a central processingunit (CPU), a microcontroller, a microprocessor, and/or other physicalhardware processor circuit. Processor circuit 1002 may execute programcode stored in a computer readable medium, such as program code ofoperating system 1030, application programs 1032, other programs 1034,etc. Bus 1006 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. System memory 1004 includes readonly memory (ROM) 1008 and random access memory (RAM) 1010. A basicinput/output system 1012 (BIOS) is stored in ROM 1008.

Computing device 1000 also has one or more of the following drives: ahard disk drive 1014 for reading from and writing to a hard disk, amagnetic disk drive 1016 for reading from or writing to a removablemagnetic disk 1018, and an optical disk drive 1020 for reading from orwriting to a removable optical disk 1022 such as a CD ROM, DVD ROM, orother optical media. Hard disk drive 1014, magnetic disk drive 1016, andoptical disk drive 1020 are connected to bus 1006 by a hard disk driveinterface 1024, a magnetic disk drive interface 1026, and an opticaldrive interface 1028, respectively. The drives and their associatedcomputer-readable media provide nonvolatile storage of computer-readableinstructions, data structures, program modules and other data for thecomputer. Although a hard disk, a removable magnetic disk and aremovable optical disk are described, other types of hardware-basedcomputer-readable storage media can be used to store data, such as flashmemory cards, digital video disks, RAMs, ROMs, and other hardwarestorage media.

A number of program modules may be stored on the hard disk, magneticdisk, optical disk, ROM, or RAM. These programs include operating system1030, one or more application programs 1032, other programs 1034, andprogram data 1036. Application programs 1032 or other programs 1034 mayinclude, for example, computer program logic (e.g., computer programcode or instructions) for implementing on-device assistant service 112,explicit signal-enabled recommendation system 114, third party service116, third party service 118, network-based assistant service 120,voiced-input interface 402, speech processing module 404, rulesgenerator 406, recommendation engine 408, output interface 410, UImodule 412, rules generator 412, recommendation engine 414, explicitsignal-enabled recommendation system 432, explicit signal-enabledrecommendation system 434, non-voiced input interface 436, NLP module606, logical statement generator 608, data converter 620, recommendationengine 802, flowchart 200, flowchart 300, flowchart 500, and/orflowchart 700 (including any suitable step of flowcharts 200, 300, 500,and 700), and/or further embodiments described herein.

A user may enter commands and information into the computing device 1000through input devices such as keyboard 1038 and pointing device 1040.Other input devices (not shown) may include a microphone, joystick, gamepad, satellite dish, scanner, a touch screen and/or touch pad, a voicerecognition system to receive voice input, a gesture recognition systemto receive gesture input, or the like. These and other input devices areoften connected to processor circuit 1002 through a serial portinterface 1042 that is coupled to bus 1006, but may be connected byother interfaces, such as a parallel port, game port, or a universalserial bus (USB).

A display screen 1044 is also connected to bus 1006 via an interface,such as a video adapter 1046. Display screen 1044 may be external to, orincorporated in computing device 1000. Display screen 1044 may displayinformation, as well as being a user interface for receiving usercommands and/or other information (e.g., by touch, finger gestures,virtual keyboard, etc.). In addition to display screen 1044, computingdevice 1000 may include other peripheral output devices (not shown) suchas speakers and printers.

Computing device 1000 is connected to a network 1048 (e.g., theInternet) through an adaptor or network interface 1050, a modem 1052, orother means for establishing communications over the network. Modem1052, which may be internal or external, may be connected to bus 1006via serial port interface 1042, as shown in FIG. 10, or may be connectedto bus 1006 using another interface type, including a parallelinterface.

As used herein, the terms “computer program medium,” “computer-readablemedium,” and “computer-readable storage medium” are used to generallyrefer to physical hardware media such as the hard disk associated withhard disk drive 1014, removable magnetic disk 1018, removable opticaldisk 1022, other physical hardware media such as RAMs, ROMs, flashmemory cards, digital video disks, zip disks, MEMs, nanotechnology-basedstorage devices, and further types of physical/tangible hardware storagemedia (including memory 920 of FIG. 9). Such computer-readable storagemedia are distinguished from and non-overlapping with communicationmedia (do not include communication media). Communication mediatypically embodies computer-readable instructions, data structures,program modules or other data in a modulated data signal such as acarrier wave. The term “modulated data signal” means a signal that hasone or more of its characteristics set or changed in such a manner as toencode information in the signal. By way of example, and not limitation,communication media includes wireless media such as acoustic, RF,infrared and other wireless media, as well as wired media. Embodimentsare also directed to such communication media.

As noted above, computer programs and modules (including applicationprograms 1032 and other programs 1034) may be stored on the hard disk,magnetic disk, optical disk, ROM, RAM, or other hardware storage medium.Such computer programs may also be received via network interface 1050,serial port interface 1042, or any other interface type. Such computerprograms, when executed or loaded by an application, enable computingdevice 1000 to implement features of embodiments discussed herein.Accordingly, such computer programs represent controllers of thecomputing device 1000.

Embodiments are also directed to computer program products comprisingcomputer code or instructions stored on any computer-readable medium.Such computer program products include hard disk drives, optical diskdrives, memory device packages, portable memory sticks, memory cards,and other types of physical storage hardware.

IV. Conclusion

While various embodiments of the present invention have been describedabove, it should be understood that they have been presented by way ofexample only, and not limitation. It will be understood by those skilledin the relevant art(s) that various changes in form and details may bemade therein without departing from the spirit and scope of theinvention as defined in the appended claims. Accordingly, the breadthand scope of the present invention should not be limited by any of theabove-described exemplary embodiments, but should be defined only inaccordance with the following claims and their equivalents.

What is claimed is:
 1. A method in a user device, comprising: receivingan explicit declaration from a user of the user device, the explicitdeclaration is configured to influence a subsequent recommendation;processing the explicit declaration to generate a record; generating arecommendation rule based on the generated record; and executing therecommendation rule to generate a recommendation for the user.
 2. Themethod of claim 1, wherein said processing the explicit declaration togenerate a record comprises: performing natural language processing on aseries of words of the explicit declaration to extract one or more keyfeatures; and generating the record to include the extracted one or morekey features.
 3. The method of claim 1, wherein said executingcomprises: matching the recommendation rule with one or moreenvironmental triggers; and executing the matching recommendation ruleto perform at least one of generating the recommendation for the user orcollecting additional data toward subsequent generation of therecommendation for the user.
 4. The method of claim 3, wherein the oneor more environmental triggers include at least one of a time, alocation, a person, an application or service, a request from the userfor data, or a request by the user for a recommendation.
 5. The methodof claim 1, wherein said executing the recommendation rule to generate arecommendation for the user comprises: issuing a query to generate queryresults; and wherein the method further comprises: providing at least aportion of the query results to the user in the recommendation.
 6. Themethod of claim 1, wherein the explicit declaration includes arecommendation provided from a person, the method further comprising:adjusting a status of the person as an influencer on the user based on areaction by the user to the recommendation provided by the person, apositive reaction by the user being indicated by at least one of theuser actioning or liking the recommendation provided by the person, anda positive reaction of the user being indicated by at least one of theuser not actioning or indicating a dislike for the recommendationprovided by the person.
 7. The method of claim 1, further comprising:launching a question and answer session with the user in response to theexplicit declaration.
 8. A recommendation system implemented in at leastone computing device, comprising: a voiced input interface configured toreceive an explicit declaration in the form of speech from a user of acomputing device, the explicit declaration includes a series of wordsand is configured to influence a subsequent recommendation; a speechprocessing module configured to process the series of words of theexplicit declaration to generate a record; a rules generator configuredto generate a recommendation rule based on the generated record, therecommendation rule including a logical expression; a recommendationengine configured to execute the recommendation rule to generate arecommendation for the user; and an output interface configured toprovide the generated recommendation to the user.
 9. The recommendationsystem of claim 8, wherein the speech processing module is configuredto: perform natural language processing on the series of words of theexplicit declaration to extract one or more key features; and generatethe record to include the extracted one or more key features.
 10. Therecommendation system of claim 8, wherein the recommendation engine isconfigured to: match the recommendation rule with one or moreenvironmental triggers; and execute the matching recommendation rule toperform at least one of generating the recommendation for the user orcollecting additional data toward subsequent generation of therecommendation for the user.
 11. The recommendation system of claim 10,wherein the one or more environmental triggers include at least one of atime, a location, a person, an application or service, a request fromthe user for data, or a request by the user for a recommendation. 12.The recommendation system of claim 8, wherein the voiced inputinterface, the speech processing module, the rules generator, and therecommendation engine are located in a user device of the user.
 13. Therecommendation system of claim 8, wherein at least one of the rulesgenerator or the recommendation engine is located at least partially ata server that is separate from a user device to which the user providedthe explicit declaration.
 16. A computer-readable storage mediumcomprising computer-executable instructions that, when executed by aprocessor, perform a method comprising: receiving an explicitdeclaration from a user of the user device, the explicit declaration isconfigured to influence a subsequent recommendation; processing theexplicit declaration to generate a record; generating a recommendationrule based on the generated record; and executing the a recommendationrule to generate a recommendation for the user.
 17. Thecomputer-readable storage medium of claim 16, wherein said executingcomprises: matching the recommendation rule with one or moreenvironmental triggers; and executing the matching recommendation ruleto perform at least one of generating the recommendation for the user orcollecting additional data toward subsequent generation of therecommendation for the user.
 18. The computer-readable storage medium ofclaim 17, wherein said executing the recommendation rule to generate arecommendation for the user comprises: issuing a query to generate queryresults; and wherein the method further comprises: providing at least aportion of the query results to the user in the recommendation.
 19. Thecomputer-readable storage medium of claim 17, wherein the explicitdeclaration includes a recommendation provided from a person, the methodfurther comprising: adjusting a status of the person as an influencer onthe user based on a reaction by the user to the recommendation providedby the person, a positive reaction by the user being indicated by atleast one of the user actioning or liking the recommendation provided bythe person, and a positive reaction of the user being indicated by atleast one of the user not actioning or indicating a dislike for therecommendation provided by the person.
 20. The computer-readable storagemedium of claim 17, further comprising: launching a question and answersession with the user in response to the explicit declaration.